This study explores how discussing metaphors for AI can help build awareness of the frames that shape our understanding of AI systems, particularly large language models (LLMs) like ChatGPT. Given the pressing need to teach "critical AI literacy", discussion of metaphor provides an opportunity for inquiry and dialogue with space for nuance, playfulness, and critique. Using a collaborative autoethnographic methodology, we analyzed metaphors from a range of sources, and reflected on them individually according to seven questions, then met and discussed our interpretations. We then analyzed how our reflections contributed to the three kinds of literacies delineated in Selber's multiliteracies framework: functional, critical, and rhetorical. These allowed us to analyze questions of ethics, equity, and accessibility in relation to AI. We explored each metaphor along the dimension of whether or not it was promoting anthropomorphizing, and to what extent such metaphors imply that AI is sentient. Our findings highlight the role of metaphor reflection in fostering a nuanced understanding of AI, suggesting that our collaborative autoethnographic approach as well as the heuristic model of plotting AI metaphors on dimensions of anthropomorphism and multiliteracies, might be useful for educators and researchers in the pursuit of advancing critical AI literacy.

Artificial intelligence (AI) and machine learning (ML) have become increasingly important in the modern business landscape, revolutionizing industries by automating processes, providing valuable insights, and enhancing decision-making. At the heart of these AI-driven solutions are machine learning engineers who play a crucial role in developing, implementing, and maintaining ML algorithms and models for various applications. As AI continues to permeate every aspect of our lives, assembling a capable and skilled AI team is more important than ever for businesses looking to stay competitive and innovative. In this blog post, we will guide you through the process of hiring a machine learning engineer for your AI team, from defining project requirements to fostering a collaborative and supportive work environment. Before starting the hiring process, it's essential to have a clear understanding of your project requirements, as this will help you identify the specific skills and expertise needed for your machine learning engineer.

While ML generates much economic value, many of us have problematic relationships with social media and other ML-powered applications. One reason is that ML often optimizes for what we want in the moment, which is easy to quantify but at odds with what is known scientifically about human flourishing. Thus, through its impoverished models of us, ML currently falls far short of its exciting potential, which is for it to help us to reach ours. While there is no consensus on defining human flourishing, from diverse perspectives across psychology, philosophy, and spiritual traditions, love is understood to be one of its primary catalysts. Motivated by this view, this paper explores whether there is a useful conception of love fitting for machines to embody, as historically it has been generative to explore whether a nebulous concept, such as life or intelligence, can be thoughtfully abstracted and reimagined, as in the fields of machine intelligence or artificial life. This paper forwards a candidate conception of machine love, inspired in particular by work in positive psychology and psychotherapy: to provide unconditional support enabling humans to autonomously pursue their own growth and development. Through proof of concept experiments, this paper aims to highlight the need for richer models of human flourishing in ML, provide an example framework through which positive psychology can be combined with ML to realize a rough conception of machine love, and demonstrate that current language models begin to enable embodying qualitative humanistic principles. The conclusion is that though at present ML may often serve to addict, distract, or divide us, an alternative path may be opening up: We may align ML to support our growth, through it helping us to align ourselves towards our highest aspirations.

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Founded in 2002, Quantium combines the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Our solutions make sense of what has happened and what will, could or should be done to re-shape industries and societies around the needs of the people they serve. As one of the world's fully diversified data science and AI leaders we operate across every sector of the economy and we're growing fast - with growth comes opportunity! We're passionate about building out our team of smart, fun, diverse and motivated people. We combine a team of experts that spans data scientists, actuaries, statisticians, business analysts, strategy consultants, engineers, technologists, programmers, product developers, and futurists – all dedicated to harnessing the power of data to drive transformational outcomes for our clients.

Applications are invited for a new prestigious DeepMind Academic Fellow in Machine Learning at Queen Mary University of London. Following a recent donation to the University from DeepMind, this three-year Fellowship is created to provide an opportunity for an excellent early career researcher in the fields of Computer Science and/or Machine Learning/ Artificial Intelligence to further their research and prepare for a full academic role within a supportive environment. The ideal candidate will have completed a PhD in a relevant field (or expect to have completed by this September) and have clear and ambitious plans for their future research, alongside the enthusiasm to act as a role model for Black researchers of the future. We particularly encourage applications from those who are in under-represented groups, and particularly those who identify as Black, as Black staff are under-represented at this level within the School of Electronic Engineering and Computer Science at Queen Mary. The Fellowship will be research-focused and the successful candidate will be allocated a research studentship to support outputs.

Are you looking for unlimited opportunities to develop and succeed? With work that challenges and makes a difference, within a flexible and supportive environment, we can help our customers achieve their dreams and aspirations. Are you looking for unlimited opportunities to develop and succeed? With work that challenges and makes a difference and a flexible and supportive environment, we can help our customers achieve their dreams and aspirations. This is a full time permanent role that can be worked from Toronto or Waterloo, Ontario.